Single Building Research Articles

Deep learning based buck-boost converter for PV modules

Over the past few years, the use of DC-DC buck-boost converters for Photovoltaic (PV) in renewable energy applications has increased for better results. One of the main issues with this type of converter is that output voltage is achieved with the undesired ripples. Many models are available in the literature to address this issue, but very limited work is available that achieves the desired goal using deep learning-based models. Whenever it comes to the PV, then it is further limited. Here, a deep learning-based model is proposed to reduce the steady-state time and achieve the desired buck- or boost mode for PV modules. The deep learning-based model is trained using data collected from the conventional PID controller. The output voltage of the experimental setup is 12V while the input voltage from the PV modules is 10V (when the sunlight decreases) to 24V (for 3.6 kVA) to 48V (for more than 5 kVA). It is among the few models using a single big battery (12V) for off-grid and on-grid for a single building. Experimental results are validated using objective measures. The proposed model outperforms the conventional PID controller-based buck-boost converters. The results clearly show improved performance in terms of steady-state and less overshoot.

Exploring and measuring the health resilience of urban buildings against the pandemic: A case study of Hong Kong public housing during COVID-19

The worldwide spread of COVID-19 raises considerable concern about occupant health and pandemic prevention. The building plays a significant role in preventing and controlling the pandemic as most of the time people stay within the built environment. Even urban resilience is widely used to cope with urban changes, the concept of resilience has seldom been adopted at the building level for occupants' health during the pandemic. There is a lack of suitable measurement methods specially designed for health resilience. Consequently, this study designs the framework of “building health resilience (BHR)” to reveal buildings’ ability to resist the pandemic spread within occupants, and develops the BHR model to measure the building performance in health resilience. The case study of Hong Kong public housing under COVID-19 reveals that implementing BHR framework and measurement approach are effective for single buildings and building groups within certain areas or during different periods. This study contributes to proposing BHR and developing BHR measurement approach by considering the building scale and external pandemic situation in nearby local area of the building. The BHR model is quite flexible to adapt to diverse disturbance scenarios to quantify the BHR performance, provides valuable results to find out weak buildings during the pandemic spread, and takes targeted actions toward healthy and resilient buildings.

Generalist versus Specialist Self-Replicators.

Darwinian evolution, including the selection of the fittest species under given environmental conditions, is a major milestone in the development of synthetic living systems. In this regard, generalist or specialist behavior (the ability to replicate in a broader or narrower, more specific food environment) are of importance. Here we demonstrate generalist and specialist behavior in dynamic combinatorial libraries composed of a peptide-based and an oligo(ethylene glycol) based building block. Three different sets of macrocyclic replicators could be distinguished based on their supramolecular organization: two prepared from a single building block as well as one prepared from an equimolar mixture of them. Peptide-containing hexamer replicators were found to be generalists, i. e. they could replicate in a broad range of food niches, whereas the octamer peptide-based replicator and hexameric ethyleneoxide-based replicator were proven to be specialists, i. e. they only replicate in very specific food niches that correspond to their composition. However, sequence specificity cannot be demonstrated for either of the generalist replicators. The generalist versus specialist nature of these replicators was linked to their supramolecular organization. Assembly modes that accommodate structurally different building blocks lead to generalist replicators, while assembly modes that are more restrictive yield specialist replicators.

Appraising the Risk Assessment of Non-Structural Components via Simplified and Machine-Learning-Based Approaches

ABSTRACT Uncertainties in building element modelling and ground motions are indispensable for rigorous seismic risk quantification. demand–intensity models simply relate building performance to intensity and subsequently risk. This article describes developments toward non-structural element (NSEs) risk quantification. The demand–intensity models are applied to infilled reinforced concrete (RC) case-study buildings. Implicit and explicit NSE numerical modelling is used to validate the models versus the direct integration of risk exceedance. For faster estimation techniques, machine learning models are trained to estimate the demand–intensity model fitting parameters on a modest dataset of RC infilled buildings. Among these, the extreme gradient boosting (XGBoost) demonstrated superior performance, indicating further possible directions for improvement where larger datasets are available. These models facilitate the simple retrieving of demand–intensity models without extensive structural analysis and can be utilized for both structural and non-structural elements when assessing risk in single or multiple buildings as part of portfolio analysis.

Multi-Dimensional Wi-Fi Received Signal Strength Indicator Data Augmentation Based on Multi-Output Gaussian Process for Large-Scale Indoor Localization

Location fingerprinting using Received Signal Strength Indicators (RSSIs) has become a popular technique for indoor localization due to its use of existing Wi-Fi infrastructure and Wi-Fi-enabled devices. Artificial intelligence/machine learning techniques such as Deep Neural Networks (DNNs) have been adopted to make location fingerprinting more accurate and reliable for large-scale indoor localization applications. However, the success of DNNs for indoor localization depends on the availability of a large amount of pre-processed and labeled data for training, the collection of which could be time-consuming in large-scale indoor environments and even challenging during a pandemic situation like COVID-19. To address these issues in data collection, we investigate multi-dimensional RSSI data augmentation based on the Multi-Output Gaussian Process (MOGP), which, unlike the Single-Output Gaussian Process (SOGP), can exploit the correlation among the RSSIs from multiple access points in a single floor, neighboring floors, or a single building by collectively processing them. The feasibility of MOGP-based multi-dimensional RSSI data augmentation is demonstrated through experiments using the hierarchical indoor localization model based on a Recurrent Neural Network (RNN)—i.e., one of the state-of-the-art multi-building and multi-floor localization models—and the publicly available UJIIndoorLoc multi-building and multi-floor indoor localization database. The RNN model trained with the UJIIndoorLoc database augmented with the augmentation mode of “by a single building”, where an MOGP model is fitted based on the entire RSSI data of a building, outperforms the other two augmentation modes and results in the three-dimensional localization error of 8.42 m.

Numerical simulation and characteristic analysis of flood intrusion into streets and buildings

In recent years, the study of urban floods has gained significant importance due to the impacts of climate change and rapid urbanization. A crucial task in accurately reproducing real-world urban flood events lies in enhancing the modeling capability to incorporate the entry of floodwaters into buildings through openings. This study presents a validated two-dimensional (2D) model based on the Shallow Water Equations (SWEs) to extend the numerical simulations beyond the analysis of water entering a single building to a cluster of buildings. The results confirm the feasibility of incorporating floodwater entering buildings processes in future urban flood simulations, thereby enhancing the realism of the outcomes. Moreover, this research delves into the characteristics of water depth and flow patterns not only on the streets but also inside the buildings. The investigation reveals that building openings play a vital role in the transportation and redistribution of floodwaters within the street network. Furthermore, variations in the rates of water level rise and recession inside buildings are observed, highlighting the influence of opening configurations and building locations. Additionally, a dynamic assessment of pedestrian risk throughout the flood duration is conducted, providing valuable insights and recommendations for disaster prevention and mitigation strategies during urban flood events. It serves as a valuable resource for urban planners and policymakers involved in the development of resilient cities in the face of increasing urban flood challenges.

Seismic Vulnerability Assessment at an Urban Scale by Means of Machine Learning Techniques

Seismic vulnerability assessment in urban areas would, in principle, require the detailed modeling of every single building and the implementation of complex numerical calculations. This procedure is clearly difficult to apply at an urban scale where many buildings must be considered; therefore, it is essential to have simplified, but at the same time reliable, approaches to vulnerability assessment. Among the proposed strategies, one of the most interesting concerns is the application of machine learning algorithms, which are able to classify buildings according to their vulnerability on the basis of training procedures applied to existing datasets. In this paper, machine learning algorithms were applied to a dataset which collects and catalogs the structural characteristics of a large number of buildings and reports the damage observed in L’Aquila territory during the intense seismic activity that occurred in 2009. A combination of a trained neural network and a random forest algorithm allows us to identify an opportune “a-posteriori” vulnerability score, deduced from the observed damage, which is compared to an “a-priori” vulnerability one, evaluated taking into account characteristic indexes for building’s typologies. By means of this comparison, an inverse approach to seismic vulnerability assessment, which can be extended to different urban centers, is proposed.

Multi-Feature Uncertainty Analysis for Urban-Scale Hypothetical 3D Reconstructions: Piazza delle Erbe Case Study

For the hypothetical reconstruction of architectural heritage, there are still no scientific standards of reference concerning their sharing and documentation. Recent international initiatives established the basis to address this problem; however, still, much work needs to be done in order to systematise good practices for the process of reconstruction and its dissemination. This contribution aims to take a step forward in the analysis and visualisation of uncertainty. Some authors have suggested various approaches to visualise uncertainty for single buildings; however, case studies at the urban scale are rarely investigated. This research proposes an improved source-based multi-feature approach aimed at analysing and visualising (through false-colour shading) the uncertainty of hypothetical 3D digital models of urban areas. The assessment of uncertainty is also quantified qualitatively by using newly defined formulas which calculate the average uncertainty weighted on the volume of the 3D model. This methodology aims to improve the objectiveness, unambiguity, transparency, reusability, and readability of hypothetical reconstructive 3D models, and its use is exemplified in the case study of the hypothetical reconstruction of Piazza delle Erbe in Verona, a project presented in the form of a docufilm at EXPO 2015 in Milan.

Pathway‐Dependent Self‐Assembly for Control over Helical Nanostructures and Topochemical Photopolymerization

AbstractPathway‐dependent self‐assembly, in which a single building block forms two or more types of self‐assembled nanostructures, is an important topic due to its mimic to the complexity in biology and manipulation of diverse supramolecular materials. Here, we report a pathway‐dependent self‐assembly using chiral glutamide derivatives (L or D‐PAG), which form chiral nanotwist and nanotube through a cooperative slow cooling and an isodesmic fast cooling process, respectively. Furthermore, pathway‐dependent self‐assembly can be harnessed to control over the supramolecular co‐assembly of PAG with a luminophore β‐DCS or a photopolymerizable PCDA. Fast cooling leads to the co‐assembled PAG/β‐DCS nanotube exhibiting green circularly polarized luminescence (CPL), while slow cooling to nanofiber with blue CPL. Additionally, fast cooling process promotes the photopolymerization of PCDA into a red chiral polymer, whereas slow cooling inhibits the polymerization. This work not only demonstrates the pathway‐dependent control over structural characteristics but also highlights the diverse functions emerged from the different assemblies.

Pathway-Dependent Self-Assembly for Control over Helical Nanostructures and Topochemical Photopolymerization.

Pathway-dependent self-assembly, in which a single building block forms two or more types of self-assembled nanostructures, is an important topic due to its mimic to the complexity in biology and manipulation of diverse supramolecular materials. Here, we report a pathway-dependent self-assembly using chiral glutamide derivatives (L or D-PAG), which form chiral nanotwist and nanotube through a cooperative slow cooling and an isodesmic fast cooling process, respectively. Furthermore, pathway-dependent self-assembly can be harnessed to control over the supramolecular co-assembly of PAG with a luminophore β-DCS or a photopolymerizable PCDA. Fast cooling leads to the co-assembled PAG/β-DCS nanotube exhibiting green circularly polarized luminescence (CPL), while slow cooling to nanofiber with blue CPL. Additionally, fast cooling process promotes the photopolymerization of PCDA into a red chiral polymer, whereas slow cooling inhibits the polymerization. This work not only demonstrates the pathway-dependent control over structural characteristics but also highlights the diverse functions emerged from the different assemblies.

Demand side management full season optimal operation potential analysis for coupled hybrid photovoltaic/thermal, heat pump, and thermal energy storage systems

In view of the excessive energy consumption caused by building demand-side matching, it is very important to combine energy storage systems and renewable energy to improve equipment energy efficiency and building energy efficiency, and correctly match demand-side management (DSM). This paper combines the energy coupling system with demand-side management to get the best balance of energy production and power grid load pressure. A photovoltaic/thermal coupling system that can be combined with heat pumps and energy storage in a single building is proposed, and a dynamic simulation model is developed to describe its operating performance. The simulation results show that the coupling system has a good energy-saving effect in the heating and cooling seasons. The energy-saving potential of the applied management strategy is 41.77 %, and the energy-saving rate is increased by 9.56 %. The cost and energy usage in different configurations are analyzed to evaluate the feasibility of combining the coupled system with demand-side management applications. It is found that the energy consumption of the coupled system is low, and the economy of its configuration selection is related to the peak and valley electricity prices, and the corresponding optimal configuration reduces the economic consumption by 58.89 % per year, which is 27.18 % less than that when the management strategy is not implemented. In addition, due to the utilization of hybrid PV/thermal energy and the smoothing effect of the temperature after heat energy storage, the payback period of the coupled system is as low as 6.23 years without relying on specific tariff factors.

Semantic Segmentation and Roof Reconstruction of Urban Buildings Based on LiDAR Point Clouds

In urban point cloud scenarios, due to the diversity of different feature types, it becomes a primary challenge to effectively obtain point clouds of building categories from urban point clouds. Therefore, this paper proposes the Enhanced Local Feature Aggregation Semantic Segmentation Network (ELFA-RandLA-Net) based on RandLA-Net, which enables ELFA-RandLA-Net to perceive local details more efficiently by learning geometric and semantic features of urban feature point clouds to achieve end-to-end building category point cloud acquisition. Then, after extracting a single building using clustering, this paper utilizes the RANSAC algorithm to segment the single building point cloud into planes and automatically identifies the roof point cloud planes according to the point cloud cloth simulation filtering principle. Finally, to solve the problem of building roof reconstruction failure due to the lack of roof vertical plane data, we introduce the roof vertical plane inference method to ensure the accuracy of roof topology reconstruction. The experiments on semantic segmentation and building reconstruction of Dublin data show that the IoU value of semantic segmentation of buildings for the ELFA-RandLA-Net network is improved by 9.11% compared to RandLA-Net. Meanwhile, the proposed building reconstruction method outperforms the classical PolyFit method.

Heat Recovery Variable Refrigerant Volume System Installation and Experiences from its Summer Operation

Variable refrigerant volume (VRV) systems operate on the principle of a cooling machine. They extract heat from one side and return it on the other, enabling them to function as a source of heat for both heating and hot water preparation, as well as a source of cold for cooling within a single building. These systems efficiently remove unnecessary excess heat outside and only introduce the required amount of heat from the outside into the building. With an appropriate configuration and setting, a VRV system can transfer heat in both directions within one system, eliminating the requirement for waste heat to be removed without use in the building. Instead, it is transferred to the desired locations where it is needed. This principle necessitates the adjustment of not only the refrigerant temperature but also its flow rate. Consequently, the VRV system can fulfil tasks that are otherwise handled by several individual systems in a building.A heat recovery VRV system was installed in a small retail store to extract waste heat generated by baking ovens during the baking process. This report provides a brief summary of electricity and energy consumption measurements taken during the summer period for cooling purposes. Sequential logic is observed and coherence is ensured, with active voice predominating for clearer and more direct communication. The parameters of interest include cooling setpoints, cooling outside of working hours, and capacity assessment.

Thermally adaptive wall to enhance indoor comfort and energy performance

In the present work, a new environment-adaptive wall is proposed, based on an inside and outside radiant panel with pipes drowned on the panels themselves and hosting a heat-carrying liquid pushed by a pump. The purpose consists of transferring heat across the thickness of the wall, in the direction required for energy saving and enhancement of inner indoor thermal comfort. A Computational Fluid Dynamic (CFD) analysis is reported and the results are applied on a single zone box-shaped building, where a whole-year study is implemented by means of the transient simulation tool TRNSYS. The efficiency of this solution respect to the state-of the-art static walls is finally discussed. A concept of a laboratory setup of an adoptive wall along with first result are presented and discussed.

Review of Everyday Modernism: Architecture & Society in Singapore

Following Singapore’s transition to self-governance in 1959 and eventual independence from British rule as a republic in 1965, growing housing needs and subsequent waves of industrialization saw the island-nation’s landscapes and coastlines radically transformed, and its population’s ways of living and working fundamentally reorganized. Everyday Modernism: Architecture and Society in Singapore makes a case for what Chang and Zhuang term the “Singapore vernacular” covering this period up to the nation’s economic prosperity in the 1990s. Putting together an insightful volume that catalogues buildings and structures representative of the time that they were constructed, the authors evoke a vernacular that refers not so much to a single style or building typology, but everyday rather than iconic architecture, at all scales, built for civic, industrial, educational, and entertainment purposes. The authors understand the everyday-ness of this architectural modernism as a concept that is “both specific and general,” which describes buildings of unknown authorship, such as those planned by architects working for government agencies, as opposed to the “heroic modernism” of contemporary projects designed by renowned local practitioners. Another facet of the everyday is the expanded sense of architecture that describes not only building projects but utilitarian structures—overhead bridges, multistorey carparks, hawker centers that house affordably priced food stalls—interwoven in the modern city’s infrastructure.

Experimental study of the turbulent characteristics in the wake of tall building clusters

This manuscript mainly explores the characteristics of turbulence quantities in the wake of tall building clusters of different array size ( $N$ ) and building spacing ( $W_S$ ) arranged in an aligned and regular grid in the flow direction. Velocity fields are measured in a wind tunnel using three-dimensional laser Doppler anemometry. Results show a delayed recovery of $u_{rms}$ and $v_{rms}$ (defined as the root-mean-square of the streamwise and lateral velocities, respectively) in the wake flow compared with the mean flow. Based on the turbulent fluctuations, the extents of the near-, transition- and far-wake regions in Mishra et al. (Boundary-Layer Meteorol., vol. 189, 2023, pp. 1–25) are revisited. In the near wake, we observe a significant reduction in $u_{rms}$ and $v_{rms}$ in the wake of a $4\times 4$ cluster compared with that of a single building. In the transition region, the turbulence intensity magnitudes within the cluster reduce to below their free-stream counterpart; this reduction is associated with the slowly varying nature of the normalised wake deficit in the streamwise direction. The recovery of the root mean square in the far-wake region is observed for $x \geq 2.5W_A$ (where $W_A$ is the width of the cluster), with the mutual interaction of the wakes formed behind the individual buildings reducing with an increase in $W_S$ , resulting in a faster recovery of the turbulent fluctuations. Finally, wavelet analysis suggests the existence of multi-scale vortex-shedding frequencies downwind of tall building clusters.

Chemically fueled dynamic switching between assembly-encoded emissions.

Self-assembly provides access to non-covalently synthesized supramolecular materials with distinct properties from a single building block. However, dynamic switching between functional states still remains challenging, but holds enormous potential in material chemistry to design smart materials. Herein, we demonstrate a chemical fuel-mediated strategy to dynamically switch between two distinctly emissive aggregates, originating from the self-assembly of a naphthalimide-appended peptide building block. A molecularly dissolved building block shows very weak blue emission, whereas, in the assembled state (Agg-1), it shows cyan emission through π stacking-mediated excimer emission. The addition of a chemical fuel, ethyl-3-(3-(dimethylamino)propyl)carbodiimide (EDC), converts the terminal aspartic acid present in the building block to an intra-molecularly cyclized anhydride in situ forming a second aggregated state, Agg-2, by changing the molecular packing, thereby transforming the emission to strong blue. Interestingly, the anhydride gets hydrolyzed gradually to reform Agg-1 and the initial cyan emission is restored. The kinetic stability of the strong blue emissive aggregate, Agg-2, can be regulated by the added concentration of the chemical fuel. Moreover, we expand the scope of this system within an agarose gel matrix, which allows us to gain spatiotemporal control over the properties, thereby producing a self-erasable writing system where the chemical fuel acts as the ink.

Soil-structure interaction effects on out-of-plane seismic response and damage of masonry buildings with shallow foundations

A significant amount of damage and casualties induced by several strong-motion earthquakes which recently stroke South-East Mediterranean area is due to the major seismic vulnerability of residential buildings. In small villages and mid-size towns, those buildings very often consist of two- to four-story, unreinforced masonry (URM) structures not designed for earthquake resistance, with direct foundations usually corresponding to an in-depth extension of load-bearing walls. For such structures, especially when founded on soft soils, site amplification and soil-foundation-structure interaction (SFSI) can significantly affect the seismic performance; conversely, such phenomena should be investigated through methods that allow a trade-off between accuracy and computational effort, hence encouraging their implementation in engineering practice. This paper provides a comprehensive updated description of the studies carried out in the last years by the authors, which are based on both linear and nonlinear, parametric, dynamic analyses of complete soil-foundation-structure (SFS) models representative of existing residential building configurations on different soils. Specifically, the parametric study investigated SFS models with different masonry types, aspect ratios, and code-conforming homogeneous and heterogeneous soil profiles. The methodology and analysis results allowed for reaching the following objectives: (i) predicting the elongation of the fundamental period and the variation of equivalent damping of the SFS system with respect to fixed-base conditions, through a simplified approach based on an equivalent simple oscillator; and (ii) estimating the probability of exceeding increasing damage levels associated with out-of-plane overturning of URM walls, through fragility functions that take into account SFS interaction. The effectiveness of these simplified tools was successfully validated against well-documented case studies, at the scales of both single instrumented buildings and urban area.

Reliability analysis of single-story industrial buildings under wind load based on Monte Carlo simulation

Uncertain disasters such as typhoons can affect buildings. Single-storey industrial building is an important type of factory building. In this paper, the reliability of a single storey industrial building under wind load is studied by taking one typical factory in Hunan, China as an example. Firstly, finite element method is used to analyse the structure in the range of linear elasticity. Then, based on Monte Carlo simulation, the probability of failure of the structure under wind load is obtained. The results show that the probability of damage is relatively small, which is also in line with the fact that inland areas are not easily affected by typhoons. In order to obtain a deeper understanding of its reliability, the structural fragility curve considering the variability of steel strength is also studied. The results showed that the smaller the variability of the steel, the more beneficial it is for the reliability of the structure.

Theories and practice

Does a theory of architecture exist? If so, what does it involve? Researching and analysing what has already been implemented? Refining methods and matrices based on practice? In other words, is the theory secondary?
 The debate about this has been going on for many years and even centuries. But today, in the age of robots and computers, the question of architectural theory takes on a new meaning. As the development of technology accelerates all practical processes – from design approval to the construction of objects in material, when million cities appear on an empty place in five years, and single buildings – in a fortnight, the practice of architecture more closely resembles a cross-country race. One can assume that the practitioner behind the wheel is increasingly in need of a theorist, a navigator who follows the road on a map and charts the course of the journey. Or do the theories in this race follow the findings and insights of the practitioners?
 In this issue of the journal, we have collected several contemporary views on architectural theory. These views are heterogeneous and often do not agree with each other. The map on which the route of architectural development is travelled is pieced together from scraps, sketches and fragments of accurate but outdated plans and outlines. But we have no other map, and we will have to improve the theory and its connection with the practice right on the move.
 It is noteworthy that the news section also touches upon the theme of the issue: it opens with the jubilee exhibitions of Maria Nashchokina, a well-known theoretician and historian of architecture, and Andrey Bokov, a prominent practitioner and theorist.